In this work, we use standard metrics to measure the performance of classifiers: sensitivity and specificity [1]}. The \(2 \times 2\)  confusion matrix showing correct predictions and types of incorrect predictions for binary classification is used to calculate sensitivity and specificity (Table REF ). The confusion matrix presents the decision made by the classifier in four categories: True positives (TP) referring to correctly labelled as positive, False positives (FP) referring to negative samples incorrectly labelled as positive, True negatives (TN) referring to negatives correctly labelled as negative and False Negatives (FN) referring to positive examples incorrectly labelled as negative [2]} .
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